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Innovation portfolio management: a systematic review and research agenda in regards to digital service innovations

• Published: 11 January 2021
• Volume 72 , pages 187–230, ( 2022 )

Cite this article

• Theresa Eckert 1 &
• Stefan Hüsig 1  

Portfolio Management (PM) for innovation is as relevant as ever before as many incumbent firms undergo massive transformation in response to digitalization and face the challenge to allocate resources for traditional product innovation and service innovation projects more efficiently and effectively. Digital service innovations, regarded as new business fields for many industrial firms are in the forefront of much discussion in practice and academia; nonetheless, it is unclear to what extent existing innovation PM has acknowledged how to manage a portfolio of service and digital service innovations. To address this gap, this work sets out to (1) review and synthesize decades of contributions in the field of innovation PM in a structured way, (2) examine to what extent research has considered and elaborated on innovation PM for services and digital services, and finally (3) provide a research agenda to foster future contributions in this field. We classified relevant findings in innovation PM into four categories (antecedents, consequences, models/frameworks, challenges) and found that literature has acknowledged services more than anticipated, but that still much of today’s innovation PM research is focused on physical products. In more recent years, the attention towards services has resulted in a few publications delving into the differences between service and product innovation PM; however, digital service innovations have been overlooked by the research so far. Lastly, we point out how innovation PM for services and digital services may diverge from traditional products and outline a research agenda.

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Eckert, T., Hüsig, S. Innovation portfolio management: a systematic review and research agenda in regards to digital service innovations. Manag Rev Q 72 , 187–230 (2022). https://doi.org/10.1007/s11301-020-00208-3

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Received : 22 May 2020

Accepted : 05 December 2020

Published : 11 January 2021

Issue Date : February 2022

DOI : https://doi.org/10.1007/s11301-020-00208-3

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Rui nuno castro , joão josé pinto ferreira.

Effective portfolio management is vital to successful product innovation. Portfolio management is about making strategic choices. It is about resource allocation. It focuses on project selection. And it deals with balance. Robert G. Cooper, Scott J. Edgett, and Elko J. Kleinschmidt

The beginning of an innovation process, also known as “front-end of innovation” (FEI), counts as an essential contributor to the successful development of new products and for their market appeal. Nevertheless, while helpful procedures and techniques for developing new products are well-known and widely applied, FEI is still an understudied area, and models for managing it are not yet commonly used in technology-oriented companies. FEI, also known as "fuzzy front end", can even be "fuzzier" in not-for-profit research centers. That is because the focus of these centers is advancing of scientific knowledge, rather than commercializing the results of those activities. This study summarizes the insights from a literature review on the topic of “project portfolio management” (PPM) in relation to innovation and, more specifically, with FEI and its components of ideation, innovation management, innovation strategy, foresight, and incremental or radical innovation. The authors selected and reviewed content from 170 papers published in SCOPUS prior to February 2019. The discussion uses a theoretical framework called "Front-End of Innovation Integrative Ontology (FEI2O)" to assist in framing the discussion.

Introduction

This study presents an integrative literature review on the processes, techniques, and capabilities of managing project portfolios, and on how they are discussed from the perspectives of innovation, ideation, and dynamic capabilities. The relationship among these topics is described in the scope of not-for-profit research centers. This study aims at addressing the problem of selecting and identifying the "best" opportunities in not-for-profit research centers that aim to impact society by transferring their R&D results to business enterprises. The purpose of not-for-profit research centers, as the name suggests, is different from that of for-profit companies. Companies target profit generation and thus innovation gets motivated by an expectation to increase sales and revenues. Not-for-profit research centers, on the other hand, are typically funded by public state budgets. They have a mission to advance knowledge, train researchers, and explore areas that may not be profitable in the short or even medium-term. However, most of these research entities seek to promote close relationships with companies, and thus enable knowledge and technology transfers.

The topic of "project portfolio management" (PPM) has been discussed and researched over the past 50 to 60 years (Zschocke et al., 2014). PPM is typically described as a process to attain four main objectives: maximize the value of a portfolio of projects, attain a balanced portfolio, make sure projects are strategically aligned, and develop the right number of projects to fit the existing resources (Cooper & Edgett, 2014). PPM targets the successful execution and development of active projects, while maintaining a balanced portfolio according to a suitable organizational strategy, with the right number of active projects and maximizing the value of the portfolio. PPM is well-established in the “new product development” (NPD) phase (Cooper et al., 2001)and already well understood in the scope of companies that develop radical or incremental products. It is less studied and applied by companies that develop new services (Aas et al., 2017), and research on the use of PPM by not-for-profit organizations still seems to be nonexistent (Barczak et al., 2006).

Not-for-profit research centers do not usually develop tangible commercial products. The "product" of a research center is commonly intangible and takes the "shape" of “intellectual property” (IP). Thus, new research projects may have several goals: to develop new IP for transferring/integrating into third party's commercial products in the future, to develop new technical and scientific competences and knowledge (thus contributing to the advancement of science and knowledge), or to develop new solutions, products, or services jointly with companies. Consequently, applying a PPM process in not-for-profit research centers may not be adequate. For example, many projects in this type of organization are publicly funded and cannot be canceled. These factors need to be considered, and research is required to find out how to adapt a PPM process to this reality. Also, the connection between the success of front-end activities and overall project success is not yet well understood (Kock et al., 2016).

Within the above context, this review article aims at understanding how to effectively manage a large number of ideas and opportunities that appear in the “front-end of innovation” (FEI) of not-for-profit R&D Centers. We present a literature review on the PPM topic that related organizational capability with the topic of “innovation”. Specifically, we focus on FEI and its components of ideation, innovation management, innovation strategy, foresight, and incremental or radical new products. The paper’s goals are: 1) to assess the available literature on both PPM and FEI, and identify insights that could be valuable to the specific context of not-for-profit research centers, 2) to discover the most relevant discussion threads relating to these topics, 3) to discover the existing gaps in the literature, 4) to unfold new research directions pointed by the authors of previous studies, and 5) to use an existing framework to organize all of the involved concepts.

This study is based on a selection and review of content in 170 publications concerning PPM and its relationship with FEI in the scope of not-for-profit research centers. The search included all available papers published in SCOPUS until February 2019. The discussion uses the so-called "Front-End of Innovation Integrative Ontology (FEI2O)" framework (Pereira et al., 2020)as a theoretical tool to assist in framing the problem. This paper writes through the use of reviews as proposed by Post, Sarala, Gatrell, and Prescott (2020), which involves looking at reviews as one possible “avenue” for advancing beneficial theory.

The paper contains five sections. The next section describes the research approach, followed by findings from the literature review in the subsequent section. Then a discussion of findings is presented, along with conclusions and ideas for further research to close to the paper.

Research Method

This study follows the “integrative literature review” approach defined by Torraco (2005). As a result, our review shows diversity and depth in the topics approached by this field. It intends to offer a novel and distinctive contribution to theory (Lepine & Wilcox-King, 2010)by relating the PPM process with FEI in not-for-profit research centers, thereby laying the ground for further development in this area.

Data collection process

Several methodologies may be used to collect data for a literature review (Crossan & Apaydin, 2010). We chose to search the Scopus database for keywords using queries shown in Table 1. Successive searches #1, #2, and #3 were done to cover different possible perspectives for paper selection. The whole process resulted in a total of 170 peer-reviewed articles, which are used in this review.

Table 1 . Data collection queries performed in Scopus

Data organization, classification, and results

We organized the articles in an electronic spreadsheet, ordered by number of citations, and categorized according to the contents of abstracts. The review was structured in a concept matrix as recommended by Webster and Watson (2002). The selected concepts were also used in the database queries. The concept of “innovation” was split as illustrated in the concept matrix outline in Figure 1. Several other words were also found to be associated with innovation. Their usage was less frequent, so we grouped them under the concept of “other innovation topics”. The frequency table used in Figure 2, as suggested by Linnenluecke and Marrone (2019), shows the number of articles found per concept.

Figure 1. Sample Concept Matrix

Figure 2 . Project Portfolio Management articles per concept

We continued this analysis by uncovering relationships among concepts, as a way to find out if one concept appears in the literature more often related to another concept. Such relationships may indicate that certain concepts cannot be dissociated from other concepts, and thus that discussions of the PPM literature need to consider multiple concepts, an approach suggested by Tranfield, Denyer, and Smart (2003). We derived the results using an aggregative approach to try to identify emerging themes. Table 2shows the number of articles that discuss PPM with two other concepts simultaneously.

Table 2 . Number of articles discussing PPM with two other concepts

The acronyms in Table 2 stand for: ID – ideation, MGMT – management, ST – strategy, NPD – new product development, FS – foresight, FEI – front-end of innovation, INC/RAD – incremental or radical, OIT – other innovation topics, DC – dynamic capabilities, RC – research center

Literature Review

We grouped the papers based on the concepts that are jointly discussed at least three times in the bibliographic database. The discussion follows the columns of Table 2. Below we identify the main threads of discussion in each group of related concepts that we found in the papers. Each group of concepts may have one or more threads of discussion.

1. Relationship between the group of concepts PPM, Ideation, and Innovation Management

Farrington, Henson, and Crews (2012)related concepts with foresight methods and discussed how these methods influence the strategic research agenda of organizations. Khameneh, Sobhiyah, and Hosseini (2016)proposed a PPM capability model where idea and proposal management is a critical capability. In another paper, from an anonymous author (2003), it was mentioned that as much as 88% of initial screening decisions made on new product projects are deficient and proposed that knowledge management solutions can enhance business performance. These findings suggest that ideation and knowledge management serve as critical capabilities of the PPM process and can have a positive influence on a company’s strategic research agenda. This is the main thread of discussion found among this group of concepts.

2. Relationship between the group of concepts PPM, Ideation, and Front-End of Innovation

Three articles discussed this topic. The authors focused on the topic of ideation portfolio management, how it affects front end performance, and how it eventually impacts the PPM process. Heising (2012)proposed a framework that shows the relationship between ideation and PPM, while Kock, Heising, and Gemünden (2015)addressed an identified research gap ( “how the management of ideation affects project performance” ) by performing an empirical cross-industry investigation. Kock, Heising, and Gemünden (2016)further discussed how researchers tend to explore the front-end from a single project perspective, instead of from a holistic perspective. The contribution of ideation portfolio management to the success of FEI activities marks a common thread of discussion found in the literature.

3. Relationship between the group of concepts PPM, Innovation Management, and Innovation Strategy

This relationship was addressed by nineteen articles. The coordination of collaborative projects and open innovation is a thread discussed by Katzy, Turgut, Holzmann, and Sailer (2013)and Brocke and Lippe (2015), which revealed that project managers tend to fail in satisfying the needs of collaborative projects. Several authors have discussed a thread on the alignment of projects with business strategy (Chao et al., 2009; de Moraes & Augusta Varela, 2013; Khameneh et al., 2016; Haghighi Rad & Rowzan, 2018). Other authors have argued, on another discussion thread, about how workshop-based road-mapping techniques may be used to address multiple management challenges, and integration in an organization’s “innovation business plan” (Farrokhzad et al., 2008; Phaal et al., 2012).

4. Relationship between the group of concepts PPM, Innovation Management, and New Product Development (NPD)

Nineteen articles also addressed this theme. A first thread of discussion is on the efficiency of R&D investments. Chao and Kavadias (2013)discussed the trade-off between how much is invested and how a firm invests money (the firm’s NPD portfolio strategy). Hughes and Chafin (1998)proposed a “value proposition life cycle” to improve the efficiency of multifunctional project teams. Schultz, Salomo, and Talke (2013)offered a scale to measure portfolio innovativeness, while Beaume, Maniak, and Midler (2009)put forward an innovation management life-cycle framework to measure the interplay between new features and new products. The topic of knowledge management is another discussion thread addressed by Cormican and O’Sullivan (2003), who focused on how to convert a company’s knowledge base into IP and new products, and on the implications of a knowledge-intensive economy on networked organizations (Cormican & O’Sullivan, 2004).

The third thread within this group of concepts unfolds on the quality of decision making. Decision making in PPM and how it is influenced by the personalities and styles of the decision-makers was discussed by Kock and Gemünden (2016)while the same problem was addressed in family firms by Kraiczy, Hack, and Kellermanns (2015). Other authors studied the decision-making process in electronics companies (Jugend et al., 2015), and checked the role of incentives and collaborative tasks in decision making (Hutchison-Krupat & Kavadias, 2018).

The last thread presents PPM as a capability to reduce time-to-market and managing scope. Ferrarese and De Carvalho (2014)proposed a tool to maximize the effective time-to-market of a portfolio given the competitive monitoring activities, and Abrantes and Figueiredo (2014)identified the challenges to manage the scope of NPD projects within the dynamic contexts that organizations face today. Country-based PPM practices in developing countries were analyzed by authors bringing forward recommendations for establishing or improving PPM capabilities in those countries’ organizations (Jugend et al., 2016;Khameneh et al., 2016).

5. Relationship between the group of concepts PPM, Innovation Management, and Incremental/Radical Innovation

Seven articles addressed this theme. The main thread of discussion in these papers was resource allocation to projects developing either radical new products or incremental new features. Similarly, the influence was shown of public incentives in the allocation of resources between projects that improve products (incremental innovation) and develop new products (radical innovation) (Chao et al., 2009). Another point of view compared how monopoly firms and their competing firms address the same problem (Zschocke et al., 2014). Other authors have offered a qualitative contribution to resource allocation based on multiple case studies (Lettice & Thomond, 2008). Finally, a discussion thread on the importance of continuous innovation as a method to battle against competitor’s disruptive innovations was highlighted as another aspect under consideration (Hughes & Chafin, 1998; Denning, 2012).

6. Relationship between the group of concepts PPM, Innovation Management and Dynamic Capabilities

The management of collaborative projects and open innovation as strategic organizational capabilities was a thread discussed by Katzy et al. (2013)who identified a gap in coordinating open innovation. These authors state that such collaboration presents specific challenges that demand adaptations and adjustments to existing project management approaches. On  another thread, PPM was considered as having a holistic capability to align projects with business strategy by Khameneh et al. (2016). These authors propose a PPM capability model that consists of eleven areas, with 81 capabilities. Other authors have treated “novelty” as a multidimensional construct (Rosenkopf & McGrath, 2011; Urhahn & Spieth, 2014;Sicotte et al., 2015). Building on the dynamic capabilities’ theory, these authors discuss the implications of portfolio innovativeness.

7. Relationship between the group of concepts PPM, Innovation Management and Research Centers

This relationship was debated in eight articles. The first thread discussed the management of collaborative projects as expressions of academia-industry interaction (Katzy et al., 2013; Brocke & Lippe, 2015). One recommendation for future research on this topic suggested that effective mechanisms are needed for project collaboration between NRIs (National Research Institutes) and for-profit organizations to maximize benefits for both parties (Jeng & Huang, 2015). Another thread of discussion was resource allocation to projects as a trade-off between incremental and radical innovation (Hendriks et al., 1999; Chao et al., 2009). On a third thread within this group of concepts, some papers have presented portfolio-building processes for evaluating project portfolios at the early initiation stage in public and not-for-profit research organizations (Pereira & Veloso, 2009; Jeng & Huang, 2015). Finally, a systematic management method for interdisciplinary research at an academic research institution-level using a co-citation index was also proposed (Kodama et al., 2013).

8. Relationship between the group of concepts PPM, Innovation Strategy, and NPD

This relationship was discussed in six articles. The single thread of discussion was on the efficiency of R&D investments. On the efficiency of PPM processes, Cooper, Edgett, and Kleinschmidt (2002), and the same authors (2000)described the importance of a Stage-Gate model, and how its correct application increases a portfolio’s value. Other authors contributed to this discussion through aligning R&D intensity with NPD portfolio efficiency, together with multifunctional project teams (Hughes & Chafin, 1998;Chao & Kavadias, 2013).

9. Relationship between the group of concepts PPM, Innovation Strategy, and Incremental/Radical Innovation

Four articles addressed this relationship. The authors addressed ways for companies to battle disruptive innovation brought up by other companies and consider alternative strategies. Denning (2012)compared continuous innovation with “good” management and concluded that continuous innovation is the most reliable strategy. Chao and Kavadias (2008)discussed, on another thread, the definition of a portfolio strategy that balances projects between incremental and radical innovation. Weigel and Goffin (2015)argued about the importance that accessing customer insights assumes in creating radical new products, services, and business models.

10. Relationship between the group of concepts PPM, Innovation Strategy, and Research Centers

Five papers related these concepts. The management of collaborative projects as forms of academia-industry interaction is a thread discussed by Katzy et al. (2013)and by Brocke and Lippe (2015). Another thread of discussion was on approaches for selecting and prioritizing IT projects in universities (Kauffmann et al., 1999; Ahriz et al., 2018). According to these authors, such approaches need to be adapted to the university's strategy, vision, and culture because university managers face many uncertainties when prioritizing projects that make up their portfolio.

11. Relationship between the group of concepts PPM, NPD, and FEI

Three papers discussed this topic. The discussion stated that the relationship with the success of the FEI is not yet fully understood (Kock et al., 2016). Cooper (2006)had previously discussed the adoption of the Stage-Gate process by technology-development companies to support front-end activities. Oliveira & Rozenfeld (2010)presented a new method to support the development of front-end activities based on PPM together with technology road-mapping (TRM). Oh, Yang, and Lee (2012)proposed a decision-making framework that uses a fuzzy expert system in PPM to deal with the uncertainty of fuzzy front-end product development.

12. Relationship between the group of concepts PPM, NPD, and Incremental/Radical Innovation

Three papers talked about this relation. The balance between incremental and radical innovation projects was the main thread discussed. This thread notes that companies face difficulties in fulfilling a balance of portfolio products, and that these difficulties possibly relate to a concentration of incremental innovation efforts in NPD (Jugend & Leoni, 2015). An innovation management life-cycle framework was proposed to measure the interplay between new features and new products (Beaume et al., 2009).  The role played by PPM in decision-making to invest in high-risk projects and how companies choose to make investments in R&D was considered critically by Cooper (2013).

We organized the main threads found in the literature review in Table 3. We identified a total of 25 discussion threads out of the 12 concept groups, though some threads appear repeatedly in the different concept groups. Thus, we grouped the 25 threads into an even number of main discussion lines. Together with this a total of seven distinct discussion threads were identifiable in Table 3.

Table 3 . List of discussion threads identified in the literature review

Even though PPM is a well-known organizational capability that has been widely applied in the NPD phase, its relationship with FEI activities is not yet fully understood (Kock et al., 2016). To contribute to rationalizing this relationship, in this discussion we use the FEI Integrative Ontology (FEI2O) proposed by Pereira et al. (2020)to frame the above findings. The FEI2O consists of a set of six sub-ontologies: FEI Purpose, FEI Portfolio Planning & Management, FEI Agile New Concept Development, FEI Stage, FEI High-Level, and FEI Actors.

The following discussion will be built around FEI2O’s sub-ontology FEI Agile New Concept Development (Figure 3) as the framework with which to overlay the above identified discussion threads. The agile nature of FEI emphasizes the need for flexibility with changing requirements and to adjust for developed concepts. It is described as an iterative process that unfolds along FEI Iterations, resulting in the development of new concepts (Pereira et al., 2020).

Figure 3 . The connections between the PPM discussion threads projected into the FEI Agile New Concept Development sub-ontology – light grey as background (A.R. Pereira et al., 2020)

The connections between the identified discussion threads and the FEI Agile NCD sub-ontology are described next. The FEI Agile NCD sub-ontology produces the [NEW CONCEPT] that will enter NPD for further commercialization. The development of new concepts in FEI is guided by the [FEI EO: Strategic Purpose] (that represents the organization’s strategy) and is framed by the [Portfolio Planning & Management] process that sets and monitors the constraints for developing new concepts. The [FEI Agile NCD] aggregates iterations [FEI Iteration] that are composed of several [BUILD], [MEASURE], and [LEARN] cycles (the [FEI learning cycle]). These cycles represent the process of building new hypotheses, testing the new hypothesis, and learning from the results of the tested hypothesis. Each iteration builds on previously existing information [Iteration information] and produces new information that will be used in subsequent iteration cycles. The [FEI STAGE] block represents activities executed in FEI (preliminary opportunity identification, product concept definition, feasibility, project planning and business model development). These activities are part of each [FEI Iteration] (each iteration contributes to improving the outcome of the activities performed in the FEI).

The following addresses the relationship between PPM issues and FEI in Figure 3:

Discussing the management of collaborative projects and open innovation serves to address research gaps in coordinating open innovation projects and in the academy-industry relationship. The influence of industry in academic projects contributes to shaping the strategic purpose [FEI EO: Strategic Purpose] of research institutes (3) because they benefit from being aligned with industry interests. This influence must also be considered in the [Portfolio Planning & Management] process (4) to find proper balance with other non-industry projects (for example, by assigning them a higher priority when allocating resources). This interaction also influences [FEI STAGE] (2) activities, for example, through identifying new opportunities, and contributes to new research concepts [FEI Agile NCD] being developed by research centers (1).

One of the PPM process objectives is to align the running projects with an organization’s strategic purpose. Discussing the alignment of PPM processes with business strategy concerns the challenge of aligning what is being done in projects with business realities. In FEI, new ideas generated must also be aligned with a organization’s overall business strategy before new concepts can be generated that will enter NPD. Therefore, aligning PPM processes with business strategy contributes to guiding the [FEI Agile NCD] (5) and to its framing by [Portfolio Planning & Management] (5).

The efficiency of R&D investments factors to influence the [FEI EO: Strategic Purpose] (6) and the [Portfolio Planning & Management] process (7). The new concepts being developed during FEI should contribute to boost sales, in the case of companies, or cause an impact on society, by not-for-profit research centers. Researchers have been looking into how to reduce the time to market for innovation while balancing increasing technological complexity (A.R. Pereira et al., 2020). Trade-off thus is available between the innovativeness of each new concept produced during FEI (that might require a higher amount of iterations) and the time to introduce new products resulting from new concepts to market (7).

Resource allocation to projects deals with finding appropriate distribution of human resources among projects, namely among projects developing radical new products, along with those developing incremental innovations that sustain the current business. While maintaining appropriate resources for incremental innovation projects, the [Portfolio Planning & Management] process (8) must leave “space” for disruptive projects too.

Converting an organization’s knowledge base and IP into new products is one of the main objectives of activities performed in the [FEI STAGE] (14). Connections (12) [FEI Iteration] and (13) the [FEI learning cycle] represent the relation between existing knowledge and new knowledge being acquired in each FEI iteration, as new knowledge is built upon existing knowledge. The new concepts [FEI Agile NCD] (11) being produced during FEI will aggregate knowledge gained from several iterations and result from a combination of new and previously existing knowledge. As the [FEI Agile NCD] is framed by the [Portfolio Planning & Management] process, a relation also forms between the existing knowledge base and this process (10). Finally, as each [FEI Iteration] adjusts the [FEI EO: Strategic purpose], the adjusted purpose also gets framed by the existing knowledge base and IP (9).

The response to competition of a new product/service concept is greatly influenced by the value proposition and its positioning in the market. This gets developed in different FEI stages, including the definition of the business model used to offer the new concept in the market (21). This issue is further related to the balance between “continuous innovation” and “good management” (Denning, 2012). There is a set of internal management decisions that provide new information to each FEI cycle [Iteration information] (15). The response to competition is “materialized” by the new concepts that emerge as the result of the activities performed in the overall FEI process resulting in the [New concept] (16).  Both [Portfolio Planning & Management] and [FEI EO: Strategic Purpose] get insights from the [Iteration information] (20). This feedback is key to adjusting strategy to the positioning of competing products and reacting to opportunities in emerging markets.

On the quality of decision making, the papers discuss the influence of different personalities and styles on the quality of the decisions that are taken. The iterative process of the FEI and the involvement of multiple actors contributes to soften those influences on new concepts that are developed during FEI. Connections (17) [FEI Iteration], (18) [Iteration information] and (19) [FEI learning cycle] show how decision making is impacted by the information used at the start of each new FEI iteration, the result of each iteration, and the aggregated effect on the FEI Agile NCD. Higher quality decisions taken at this stage typically build on internal knowledge as well as on external primary and secondary sources (for example, FEI learning cycle), higher quality concepts (a.k.a. new knowledge, or new IP, or new prototypes) develop into products, and more likely innovations increase the portfolio’s innovativeness. Adequate ideation of portfolio management may also help increase decision quality and reduce the time-to-market (Heising, 2012), a crucial feature in today’s competitive world.

In research centers, the [New Concept] module could be renamed as [New Research Concept] in order to focus on R&D efforts. FEI outcomes may be seen as new ideas/concepts whose feasibility still needs to be assessed before entering the [New Research Development] phase. The discussion of not-for-profit research centers appears mostly related to managing collaborative projects and open innovation, along with interaction between academia and industry. This interaction influences the research center’s [FEI EO: Strategic Purpose] and its [Portfolio Planning & Management] process. The activities performed by research centers during FEI must address the challenges of collaborative projects and use the information provided by projects in the [BUILD], [MEASURE] and [LEARN] cycles performed at each [FEI ITERATION]. In the end, new research concepts that are investigated will benefit from close interaction with industry, which is represented in Figure 3by connections between the “Research Institutes” block and the different components of the FEI Agile NCD sub-ontology.

Conclusion and Suggestions for Further Research

The discussion threads revealed by this study address all aspects considered in the FEI2O Agile NCD sub-ontology: PPM is an organizational capability that makes sure that both existing and new projects are aligned with business strategy. PPM also ensures that resources get allocated according to a defined strategy and that senior management involvement as an organizational factor drives PPM success.

Kock, Heising, and Gemünden (2016)showed that front-end success is highly essential for later project success. We support this conclusion that brings in the importance of ideation portfolio management. These authors underscore the importance of an effective and efficient FEI for project portfolio success in generating the right ideas.

The research also showed that PPM is widely discussed from the perspective of innovation management, innovation strategy, and NPD. PPM has been a research area for over 50 years (Zschocke et al., 2014)and has been one of the critical components of the Innovation Management field. Despite that, it has been a discipline used mostly by private companies (Pereira & Veloso, 2009)that focus on developing incremental or radical new products. As such, we conducted an integrative literature review to uncover the usage of PPM in research centers and universities because these entities are partnering more and more with companies to develop innovative new products and services.

As shown by Katzy et al. (2013)and Brocke and Lippe (2015), managing collaborative projects as a form of academy-industry interaction is still an understudied area of project management. Traditional project management methods have tended to fail when dealing with the specificities of these types of projects. The same authors point to the need for further research to generalize the findings and to chart the historical development of coordinating innovation processes. Based on the growing importance of open innovation and in cooperation with not-for-profit research centers and companies, we believe future research could be beneficial by promoting a more holistic understanding of how research centers manage their FEI. More specifically, we wish to inquire how research centers prioritize research project ideas, how they measure the success of front-end activities, and how they manage collaborative projects with industry.

Our analysis of the various PPM discussion threads framed together with the FEI Agile New Concept Development sub-ontology reveals that existing research does not highlight R&D as a critically relevant activity for FEI. This constitutes one of the study’s main findings, a surprise considering that both the efficiency of R&D investments and resource allocation to projects were subjects of research. The relevance of R&D activities for the FEI and the organization of the FEI in research centers is left as a suggestion for further research.

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• Published: 31 August 2024

Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023

• Xianru Shang   ORCID: orcid.org/0009-0000-8906-3216 1 ,
• Zijian Liu 1 ,
• Chen Gong 1 ,
• Zhigang Hu 1 ,
• Yuexuan Wu 1 &
• Chengliang Wang   ORCID: orcid.org/0000-0003-2208-3508 2  

Humanities and Social Sciences Communications volume  11 , Article number:  1115 ( 2024 ) Cite this article

Metrics details

• Science, technology and society

The rapid expansion of information technology and the intensification of population aging are two prominent features of contemporary societal development. Investigating older adults’ acceptance and use of technology is key to facilitating their integration into an information-driven society. Given this context, the technology acceptance of older adults has emerged as a prioritized research topic, attracting widespread attention in the academic community. However, existing research remains fragmented and lacks a systematic framework. To address this gap, we employed bibliometric methods, utilizing the Web of Science Core Collection to conduct a comprehensive review of literature on older adults’ technology acceptance from 2013 to 2023. Utilizing VOSviewer and CiteSpace for data assessment and visualization, we created knowledge mappings of research on older adults’ technology acceptance. Our study employed multidimensional methods such as co-occurrence analysis, clustering, and burst analysis to: (1) reveal research dynamics, key journals, and domains in this field; (2) identify leading countries, their collaborative networks, and core research institutions and authors; (3) recognize the foundational knowledge system centered on theoretical model deepening, emerging technology applications, and research methods and evaluation, uncovering seminal literature and observing a shift from early theoretical and influential factor analyses to empirical studies focusing on individual factors and emerging technologies; (4) moreover, current research hotspots are primarily in the areas of factors influencing technology adoption, human-robot interaction experiences, mobile health management, and aging-in-place technology, highlighting the evolutionary context and quality distribution of research themes. Finally, we recommend that future research should deeply explore improvements in theoretical models, long-term usage, and user experience evaluation. Overall, this study presents a clear framework of existing research in the field of older adults’ technology acceptance, providing an important reference for future theoretical exploration and innovative applications.

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Introduction.

In contemporary society, the rapid development of information technology has been intricately intertwined with the intensifying trend of population aging. According to the latest United Nations forecast, by 2050, the global population aged 65 and above is expected to reach 1.6 billion, representing about 16% of the total global population (UN 2023 ). Given the significant challenges of global aging, there is increasing evidence that emerging technologies have significant potential to maintain health and independence for older adults in their home and healthcare environments (Barnard et al. 2013 ; Soar 2010 ; Vancea and Solé-Casals 2016 ). This includes, but is not limited to, enhancing residential safety with smart home technologies (Touqeer et al. 2021 ; Wang et al. 2022 ), improving living independence through wearable technologies (Perez et al. 2023 ), and increasing medical accessibility via telehealth services (Kruse et al. 2020 ). Technological innovations are redefining the lifestyles of older adults, encouraging a shift from passive to active participation (González et al. 2012 ; Mostaghel 2016 ). Nevertheless, the effective application and dissemination of technology still depends on user acceptance and usage intentions (Naseri et al. 2023 ; Wang et al. 2023a ; Xia et al. 2024 ; Yu et al. 2023 ). Particularly, older adults face numerous challenges in accepting and using new technologies. These challenges include not only physical and cognitive limitations but also a lack of technological experience, along with the influences of social and economic factors (Valk et al. 2018 ; Wilson et al. 2021 ).

User acceptance of technology is a significant focus within information systems (IS) research (Dai et al. 2024 ), with several models developed to explain and predict user behavior towards technology usage, including the Technology Acceptance Model (TAM) (Davis 1989 ), TAM2, TAM3, and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al. 2003 ). Older adults, as a group with unique needs, exhibit different behavioral patterns during technology acceptance than other user groups, and these uniquenesses include changes in cognitive abilities, as well as motivations, attitudes, and perceptions of the use of new technologies (Chen and Chan 2011 ). The continual expansion of technology introduces considerable challenges for older adults, rendering the understanding of their technology acceptance a research priority. Thus, conducting in-depth research into older adults’ acceptance of technology is critically important for enhancing their integration into the information society and improving their quality of life through technological advancements.

Reviewing relevant literature to identify research gaps helps further solidify the theoretical foundation of the research topic. However, many existing literature reviews primarily focus on the factors influencing older adults’ acceptance or intentions to use technology. For instance, Ma et al. ( 2021 ) conducted a comprehensive analysis of the determinants of older adults’ behavioral intentions to use technology; Liu et al. ( 2022 ) categorized key variables in studies of older adults’ technology acceptance, noting a shift in focus towards social and emotional factors; Yap et al. ( 2022 ) identified seven categories of antecedents affecting older adults’ use of technology from an analysis of 26 articles, including technological, psychological, social, personal, cost, behavioral, and environmental factors; Schroeder et al. ( 2023 ) extracted 119 influencing factors from 59 articles and further categorized these into six themes covering demographics, health status, and emotional awareness. Additionally, some studies focus on the application of specific technologies, such as Ferguson et al. ( 2021 ), who explored barriers and facilitators to older adults using wearable devices for heart monitoring, and He et al. ( 2022 ) and Baer et al. ( 2022 ), who each conducted in-depth investigations into the acceptance of social assistive robots and mobile nutrition and fitness apps, respectively. In summary, current literature reviews on older adults’ technology acceptance exhibit certain limitations. Due to the interdisciplinary nature and complex knowledge structure of this field, traditional literature reviews often rely on qualitative analysis, based on literature analysis and periodic summaries, which lack sufficient objectivity and comprehensiveness. Additionally, systematic research is relatively limited, lacking a macroscopic description of the research trajectory from a holistic perspective. Over the past decade, research on older adults’ technology acceptance has experienced rapid growth, with a significant increase in literature, necessitating the adoption of new methods to review and examine the developmental trends in this field (Chen 2006 ; Van Eck and Waltman 2010 ). Bibliometric analysis, as an effective quantitative research method, analyzes published literature through visualization, offering a viable approach to extracting patterns and insights from a large volume of papers, and has been widely applied in numerous scientific research fields (Achuthan et al. 2023 ; Liu and Duffy 2023 ). Therefore, this study will employ bibliometric methods to systematically analyze research articles related to older adults’ technology acceptance published in the Web of Science Core Collection from 2013 to 2023, aiming to understand the core issues and evolutionary trends in the field, and to provide valuable references for future related research. Specifically, this study aims to explore and answer the following questions:

RQ1: What are the research dynamics in the field of older adults’ technology acceptance over the past decade? What are the main academic journals and fields that publish studies related to older adults’ technology acceptance?

RQ2: How is the productivity in older adults’ technology acceptance research distributed among countries, institutions, and authors?

RQ3: What are the knowledge base and seminal literature in older adults’ technology acceptance research? How has the research theme progressed?

RQ4: What are the current hot topics and their evolutionary trajectories in older adults’ technology acceptance research? How is the quality of research distributed?

Methodology and materials

Research method.

In recent years, bibliometrics has become one of the crucial methods for analyzing literature reviews and is widely used in disciplinary and industrial intelligence analysis (Jing et al. 2023 ; Lin and Yu 2024a ; Wang et al. 2024a ; Xu et al. 2021 ). Bibliometric software facilitates the visualization analysis of extensive literature data, intuitively displaying the network relationships and evolutionary processes between knowledge units, and revealing the underlying knowledge structure and potential information (Chen et al. 2024 ; López-Robles et al. 2018 ; Wang et al. 2024c ). This method provides new insights into the current status and trends of specific research areas, along with quantitative evidence, thereby enhancing the objectivity and scientific validity of the research conclusions (Chen et al. 2023 ; Geng et al. 2024 ). VOSviewer and CiteSpace are two widely used bibliometric software tools in academia (Pan et al. 2018 ), recognized for their robust functionalities based on the JAVA platform. Although each has its unique features, combining these two software tools effectively constructs mapping relationships between literature knowledge units and clearly displays the macrostructure of the knowledge domains. Particularly, VOSviewer, with its excellent graphical representation capabilities, serves as an ideal tool for handling large datasets and precisely identifying the focal points and hotspots of research topics. Therefore, this study utilizes VOSviewer (version 1.6.19) and CiteSpace (version 6.1.R6), combined with in-depth literature analysis, to comprehensively examine and interpret the research theme of older adults’ technology acceptance through an integrated application of quantitative and qualitative methods.

Data source

Web of Science is a comprehensively recognized database in academia, featuring literature that has undergone rigorous peer review and editorial scrutiny (Lin and Yu 2024b ; Mongeon and Paul-Hus 2016 ; Pranckutė 2021 ). This study utilizes the Web of Science Core Collection as its data source, specifically including three major citation indices: Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI). These indices encompass high-quality research literature in the fields of science, social sciences, and arts and humanities, ensuring the comprehensiveness and reliability of the data. We combined “older adults” with “technology acceptance” through thematic search, with the specific search strategy being: TS = (elder OR elderly OR aging OR ageing OR senile OR senior OR old people OR “older adult*”) AND TS = (“technology acceptance” OR “user acceptance” OR “consumer acceptance”). The time span of literature search is from 2013 to 2023, with the types limited to “Article” and “Review” and the language to “English”. Additionally, the search was completed by October 27, 2023, to avoid data discrepancies caused by database updates. The initial search yielded 764 journal articles. Given that searches often retrieve articles that are superficially relevant but actually non-compliant, manual screening post-search was essential to ensure the relevance of the literature (Chen et al. 2024 ). Through manual screening, articles significantly deviating from the research theme were eliminated and rigorously reviewed. Ultimately, this study obtained 500 valid sample articles from the Web of Science Core Collection. The complete PRISMA screening process is illustrated in Fig. 1 .

Presentation of the data culling process in detail.

Data standardization

Raw data exported from databases often contain multiple expressions of the same terminology (Nguyen and Hallinger 2020 ). To ensure the accuracy and consistency of data, it is necessary to standardize the raw data (Strotmann and Zhao 2012 ). This study follows the data standardization process proposed by Taskin and Al ( 2019 ), mainly executing the following operations:

(1) Standardization of author and institution names is conducted to address different name expressions for the same author. For instance, “Chan, Alan Hoi Shou” and “Chan, Alan H. S.” are considered the same author, and distinct authors with the same name are differentiated by adding identifiers. Diverse forms of institutional names are unified to address variations caused by name changes or abbreviations, such as standardizing “FRANKFURT UNIV APPL SCI” and “Frankfurt University of Applied Sciences,” as well as “Chinese University of Hong Kong” and “University of Hong Kong” to consistent names.

(2) Different expressions of journal names are unified. For example, “International Journal of Human-Computer Interaction” and “Int J Hum Comput Interact” are standardized to a single name. This ensures consistency in journal names and prevents misclassification of literature due to differing journal names. Additionally, it involves checking if the journals have undergone name changes in the past decade to prevent any impact on the analysis due to such changes.

(3) Keywords data are cleansed by removing words that do not directly pertain to specific research content (e.g., people, review), merging synonyms (e.g., “UX” and “User Experience,” “aging-in-place” and “aging in place”), and standardizing plural forms of keywords (e.g., “assistive technologies” and “assistive technology,” “social robots” and “social robot”). This reduces redundant information in knowledge mapping.

Bibliometric results and analysis

Distribution power (rq1), literature descriptive statistical analysis.

Table 1 presents a detailed descriptive statistical overview of the literature in the field of older adults’ technology acceptance. After deduplication using the CiteSpace software, this study confirmed a valid sample size of 500 articles. Authored by 1839 researchers, the documents encompass 792 research institutions across 54 countries and are published in 217 different academic journals. As of the search cutoff date, these articles have accumulated 13,829 citations, with an annual average of 1156 citations, and an average of 27.66 citations per article. The h-index, a composite metric of quantity and quality of scientific output (Kamrani et al. 2021 ), reached 60 in this study.

Trends in publications and disciplinary distribution

The number of publications and citations are significant indicators of the research field’s development, reflecting its continuity, attention, and impact (Ale Ebrahim et al. 2014 ). The ranking of annual publications and citations in the field of older adults’ technology acceptance studies is presented chronologically in Fig. 2A . The figure shows a clear upward trend in the amount of literature in this field. Between 2013 and 2017, the number of publications increased slowly and decreased in 2018. However, in 2019, the number of publications increased rapidly to 52 and reached a peak of 108 in 2022, which is 6.75 times higher than in 2013. In 2022, the frequency of document citations reached its highest point with 3466 citations, reflecting the widespread recognition and citation of research in this field. Moreover, the curve of the annual number of publications fits a quadratic function, with a goodness-of-fit R 2 of 0.9661, indicating that the number of future publications is expected to increase even more rapidly.

A Trends in trends in annual publications and citations (2013–2023). B Overlay analysis of the distribution of discipline fields.

Figure 2B shows that research on older adults’ technology acceptance involves the integration of multidisciplinary knowledge. According to Web of Science Categories, these 500 articles are distributed across 85 different disciplines. We have tabulated the top ten disciplines by publication volume (Table 2 ), which include Medical Informatics (75 articles, 15.00%), Health Care Sciences & Services (71 articles, 14.20%), Gerontology (61 articles, 12.20%), Public Environmental & Occupational Health (57 articles, 11.40%), and Geriatrics & Gerontology (52 articles, 10.40%), among others. The high output in these disciplines reflects the concentrated global academic interest in this comprehensive research topic. Additionally, interdisciplinary research approaches provide diverse perspectives and a solid theoretical foundation for studies on older adults’ technology acceptance, also paving the way for new research directions.

Knowledge flow analysis

A dual-map overlay is a CiteSpace map superimposed on top of a base map, which shows the interrelationships between journals in different domains, representing the publication and citation activities in each domain (Chen and Leydesdorff 2014 ). The overlay map reveals the link between the citing domain (on the left side) and the cited domain (on the right side), reflecting the knowledge flow of the discipline at the journal level (Leydesdorff and Rafols 2012 ). We utilize the in-built Z-score algorithm of the software to cluster the graph, as shown in Fig. 3 .

The left side shows the citing journal, and the right side shows the cited journal.

Figure 3 shows the distribution of citing journals clusters for older adults’ technology acceptance on the left side, while the right side refers to the main cited journals clusters. Two knowledge flow citation trajectories were obtained; they are presented by the color of the cited regions, and the thickness of these trajectories is proportional to the Z-score scaled frequency of citations (Chen et al. 2014 ). Within the cited regions, the most popular fields with the most records covered are “HEALTH, NURSING, MEDICINE” and “PSYCHOLOGY, EDUCATION, SOCIAL”, and the elliptical aspect ratio of these two fields stands out. Fields have prominent elliptical aspect ratios, highlighting their significant influence on older adults’ technology acceptance research. Additionally, the major citation trajectories originate in these two areas and progress to the frontier research area of “PSYCHOLOGY, EDUCATION, HEALTH”. It is worth noting that the citation trajectory from “PSYCHOLOGY, EDUCATION, SOCIAL” has a significant Z-value (z = 6.81), emphasizing the significance and impact of this development path. In the future, “MATHEMATICS, SYSTEMS, MATHEMATICAL”, “MOLECULAR, BIOLOGY, IMMUNOLOGY”, and “NEUROLOGY, SPORTS, OPHTHALMOLOGY” may become emerging fields. The fields of “MEDICINE, MEDICAL, CLINICAL” may be emerging areas of cutting-edge research.

Main research journals analysis

Table 3 provides statistics for the top ten journals by publication volume in the field of older adults’ technology acceptance. Together, these journals have published 137 articles, accounting for 27.40% of the total publications, indicating that there is no highly concentrated core group of journals in this field, with publications being relatively dispersed. Notably, Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction each lead with 15 publications. In terms of citation metrics, International Journal of Medical Informatics and Computers in Human Behavior stand out significantly, with the former accumulating a total of 1,904 citations, averaging 211.56 citations per article, and the latter totaling 1,449 citations, with an average of 96.60 citations per article. These figures emphasize the academic authority and widespread impact of these journals within the research field.

Research power (RQ2)

Countries and collaborations analysis.

The analysis revealed the global research pattern for country distribution and collaboration (Chen et al. 2019 ). Figure 4A shows the network of national collaborations on older adults’ technology acceptance research. The size of the bubbles represents the amount of publications in each country, while the thickness of the connecting lines expresses the closeness of the collaboration among countries. Generally, this research subject has received extensive international attention, with China and the USA publishing far more than any other countries. China has established notable research collaborations with the USA, UK and Malaysia in this field, while other countries have collaborations, but the closeness is relatively low and scattered. Figure 4B shows the annual publication volume dynamics of the top ten countries in terms of total publications. Since 2017, China has consistently increased its annual publications, while the USA has remained relatively stable. In 2019, the volume of publications in each country increased significantly, this was largely due to the global outbreak of the COVID-19 pandemic, which has led to increased reliance on information technology among the elderly for medical consultations, online socialization, and health management (Sinha et al. 2021 ). This phenomenon has led to research advances in technology acceptance among older adults in various countries. Table 4 shows that the top ten countries account for 93.20% of the total cumulative number of publications, with each country having published more than 20 papers. Among these ten countries, all of them except China are developed countries, indicating that the research field of older adults’ technology acceptance has received general attention from developed countries. Currently, China and the USA were the leading countries in terms of publications with 111 and 104 respectively, accounting for 22.20% and 20.80%. The UK, Germany, Italy, and the Netherlands also made significant contributions. The USA and China ranked first and second in terms of the number of citations, while the Netherlands had the highest average citations, indicating the high impact and quality of its research. The UK has shown outstanding performance in international cooperation, while the USA highlights its significant academic influence in this field with the highest h-index value.

A National collaboration network. B Annual volume of publications in the top 10 countries.

Institutions and authors analysis

Analyzing the number of publications and citations can reveal an institution’s or author’s research strength and influence in a particular research area (Kwiek 2021 ). Tables 5 and 6 show the statistics of the institutions and authors whose publication counts are in the top ten, respectively. As shown in Table 5 , higher education institutions hold the main position in this research field. Among the top ten institutions, City University of Hong Kong and The University of Hong Kong from China lead with 14 and 9 publications, respectively. City University of Hong Kong has the highest h-index, highlighting its significant influence in the field. It is worth noting that Tilburg University in the Netherlands is not among the top five in terms of publications, but the high average citation count (130.14) of its literature demonstrates the high quality of its research.

After analyzing the authors’ output using Price’s Law (Redner 1998 ), the highest number of publications among the authors counted ( n  = 10) defines a publication threshold of 3 for core authors in this research area. As a result of quantitative screening, a total of 63 core authors were identified. Table 6 shows that Chen from Zhejiang University, China, Ziefle from RWTH Aachen University, Germany, and Rogers from Macquarie University, Australia, were the top three authors in terms of the number of publications, with 10, 9, and 8 articles, respectively. In terms of average citation rate, Peek and Wouters, both scholars from the Netherlands, have significantly higher rates than other scholars, with 183.2 and 152.67 respectively. This suggests that their research is of high quality and widely recognized. Additionally, Chen and Rogers have high h-indices in this field.

Knowledge base and theme progress (RQ3)

Research knowledge base.

Co-citation relationships occur when two documents are cited together (Zhang and Zhu 2022 ). Co-citation mapping uses references as nodes to represent the knowledge base of a subject area (Min et al. 2021). Figure 5A illustrates co-occurrence mapping in older adults’ technology acceptance research, where larger nodes signify higher co-citation frequencies. Co-citation cluster analysis can be used to explore knowledge structure and research boundaries (Hota et al. 2020 ; Shiau et al. 2023 ). The co-citation clustering mapping of older adults’ technology acceptance research literature (Fig. 5B ) shows that the Q value of the clustering result is 0.8129 (>0.3), and the average value of the weight S is 0.9391 (>0.7), indicating that the clusters are uniformly distributed with a significant and credible structure. This further proves that the boundaries of the research field are clear and there is significant differentiation in the field. The figure features 18 cluster labels, each associated with thematic color blocks corresponding to different time slices. Highlighted emerging research themes include #2 Smart Home Technology, #7 Social Live, and #10 Customer Service. Furthermore, the clustering labels extracted are primarily classified into three categories: theoretical model deepening, emerging technology applications, research methods and evaluation, as detailed in Table 7 .

A Co-citation analysis of references. B Clustering network analysis of references.

Seminal literature analysis

The top ten nodes in terms of co-citation frequency were selected for further analysis. Table 8 displays the corresponding node information. Studies were categorized into four main groups based on content analysis. (1) Research focusing on specific technology usage by older adults includes studies by Peek et al. ( 2014 ), Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ), who investigated the factors influencing the use of e-technology, smartphones, mHealth, and smart wearables, respectively. (2) Concerning the development of theoretical models of technology acceptance, Chen and Chan ( 2014 ) introduced the Senior Technology Acceptance Model (STAM), and Macedo ( 2017 ) analyzed the predictive power of UTAUT2 in explaining older adults’ intentional behaviors and information technology usage. (3) In exploring older adults’ information technology adoption and behavior, Lee and Coughlin ( 2015 ) emphasized that the adoption of technology by older adults is a multifactorial process that includes performance, price, value, usability, affordability, accessibility, technical support, social support, emotion, independence, experience, and confidence. Yusif et al. ( 2016 ) conducted a literature review examining the key barriers affecting older adults’ adoption of assistive technology, including factors such as privacy, trust, functionality/added value, cost, and stigma. (4) From the perspective of research into older adults’ technology acceptance, Mitzner et al. ( 2019 ) assessed the long-term usage of computer systems designed for the elderly, whereas Guner and Acarturk ( 2020 ) compared information technology usage and acceptance between older and younger adults. The breadth and prevalence of this literature make it a vital reference for researchers in the field, also providing new perspectives and inspiration for future research directions.

Research thematic progress

Burst citation is a node of literature that guides the sudden change in dosage, which usually represents a prominent development or major change in a particular field, with innovative and forward-looking qualities. By analyzing the emergent literature, it is often easy to understand the dynamics of the subject area, mapping the emerging thematic change (Chen et al. 2022 ). Figure 6 shows the burst citation mapping in the field of older adults’ technology acceptance research, with burst citations represented by red nodes (Fig. 6A ). For the ten papers with the highest burst intensity (Fig. 6B ), this study will conduct further analysis in conjunction with literature review.

A Burst detection of co-citation. B The top 10 references with the strongest citation bursts.

As shown in Fig. 6 , Mitzner et al. ( 2010 ) broke the stereotype that older adults are fearful of technology, found that they actually have positive attitudes toward technology, and emphasized the centrality of ease of use and usefulness in the process of technology acceptance. This finding provides an important foundation for subsequent research. During the same period, Wagner et al. ( 2010 ) conducted theory-deepening and applied research on technology acceptance among older adults. The research focused on older adults’ interactions with computers from the perspective of Social Cognitive Theory (SCT). This expanded the understanding of technology acceptance, particularly regarding the relationship between behavior, environment, and other SCT elements. In addition, Pan and Jordan-Marsh ( 2010 ) extended the TAM to examine the interactions among predictors of perceived usefulness, perceived ease of use, subjective norm, and convenience conditions when older adults use the Internet, taking into account the moderating roles of gender and age. Heerink et al. ( 2010 ) adapted and extended the UTAUT, constructed a technology acceptance model specifically designed for older users’ acceptance of assistive social agents, and validated it using controlled experiments and longitudinal data, explaining intention to use by combining functional assessment and social interaction variables.

Then the research theme shifted to an in-depth analysis of the factors influencing technology acceptance among older adults. Two papers with high burst strengths emerged during this period: Peek et al. ( 2014 ) (Strength = 12.04), Chen and Chan ( 2014 ) (Strength = 9.81). Through a systematic literature review and empirical study, Peek STM and Chen K, among others, identified multidimensional factors that influence older adults’ technology acceptance. Peek et al. ( 2014 ) analyzed literature on the acceptance of in-home care technology among older adults and identified six factors that influence their acceptance: concerns about technology, expected benefits, technology needs, technology alternatives, social influences, and older adult characteristics, with a focus on differences between pre- and post-implementation factors. Chen and Chan ( 2014 ) constructed the STAM by administering a questionnaire to 1012 older adults and adding eight important factors, including technology anxiety, self-efficacy, cognitive ability, and physical function, based on the TAM. This enriches the theoretical foundation of the field. In addition, Braun ( 2013 ) highlighted the role of perceived usefulness, trust in social networks, and frequency of Internet use in older adults’ use of social networks, while ease of use and social pressure were not significant influences. These findings contribute to the study of older adults’ technology acceptance within specific technology application domains.

Recent research has focused on empirical studies of personal factors and emerging technologies. Ma et al. ( 2016 ) identified key personal factors affecting smartphone acceptance among older adults through structured questionnaires and face-to-face interviews with 120 participants. The study found that cost, self-satisfaction, and convenience were important factors influencing perceived usefulness and ease of use. This study offers empirical evidence to comprehend the main factors that drive smartphone acceptance among Chinese older adults. Additionally, Yusif et al. ( 2016 ) presented an overview of the obstacles that hinder older adults’ acceptance of assistive technologies, focusing on privacy, trust, and functionality.

In summary, research on older adults’ technology acceptance has shifted from early theoretical deepening and analysis of influencing factors to empirical studies in the areas of personal factors and emerging technologies, which have greatly enriched the theoretical basis of older adults’ technology acceptance and provided practical guidance for the design of emerging technology products.

Research hotspots, evolutionary trends, and quality distribution (RQ4)

Core keywords analysis.

Keywords concise the main idea and core of the literature, and are a refined summary of the research content (Huang et al. 2021 ). In CiteSpace, nodes with a centrality value greater than 0.1 are considered to be critical nodes. Analyzing keywords with high frequency and centrality helps to visualize the hot topics in the research field (Park et al. 2018 ). The merged keywords were imported into CiteSpace, and the top 10 keywords were counted and sorted by frequency and centrality respectively, as shown in Table 9 . The results show that the keyword “TAM” has the highest frequency (92), followed by “UTAUT” (24), which reflects that the in-depth study of the existing technology acceptance model and its theoretical expansion occupy a central position in research related to older adults’ technology acceptance. Furthermore, the terms ‘assistive technology’ and ‘virtual reality’ are both high-frequency and high-centrality terms (frequency = 17, centrality = 0.10), indicating that the research on assistive technology and virtual reality for older adults is the focus of current academic attention.

Research hotspots analysis

Using VOSviewer for keyword co-occurrence analysis organizes keywords into groups or clusters based on their intrinsic connections and frequencies, clearly highlighting the research field’s hot topics. The connectivity among keywords reveals correlations between different topics. To ensure accuracy, the analysis only considered the authors’ keywords. Subsequently, the keywords were filtered by setting the keyword frequency to 5 to obtain the keyword clustering map of the research on older adults’ technology acceptance research keyword clustering mapping (Fig. 7 ), combined with the keyword co-occurrence clustering network (Fig. 7A ) and the corresponding density situation (Fig. 7B ) to make a detailed analysis of the following four groups of clustered themes.

A Co-occurrence clustering network. B Keyword density.

Cluster #1—Research on the factors influencing technology adoption among older adults is a prominent topic, covering age, gender, self-efficacy, attitude, and and intention to use (Berkowsky et al. 2017 ; Wang et al. 2017 ). It also examined older adults’ attitudes towards and acceptance of digital health technologies (Ahmad and Mozelius, 2022 ). Moreover, the COVID-19 pandemic, significantly impacting older adults’ technology attitudes and usage, has underscored the study’s importance and urgency. Therefore, it is crucial to conduct in-depth studies on how older adults accept, adopt, and effectively use new technologies, to address their needs and help them overcome the digital divide within digital inclusion. This will improve their quality of life and healthcare experiences.

Cluster #2—Research focuses on how older adults interact with assistive technologies, especially assistive robots and health monitoring devices, emphasizing trust, usability, and user experience as crucial factors (Halim et al. 2022 ). Moreover, health monitoring technologies effectively track and manage health issues common in older adults, like dementia and mild cognitive impairment (Lussier et al. 2018 ; Piau et al. 2019 ). Interactive exercise games and virtual reality have been deployed to encourage more physical and cognitive engagement among older adults (Campo-Prieto et al. 2021 ). Personalized and innovative technology significantly enhances older adults’ participation, improving their health and well-being.

Cluster #3—Optimizing health management for older adults using mobile technology. With the development of mobile health (mHealth) and health information technology, mobile applications, smartphones, and smart wearable devices have become effective tools to help older users better manage chronic conditions, conduct real-time health monitoring, and even receive telehealth services (Dupuis and Tsotsos 2018 ; Olmedo-Aguirre et al. 2022 ; Kim et al. 2014 ). Additionally, these technologies can mitigate the problem of healthcare resource inequality, especially in developing countries. Older adults’ acceptance and use of these technologies are significantly influenced by their behavioral intentions, motivational factors, and self-management skills. These internal motivational factors, along with external factors, jointly affect older adults’ performance in health management and quality of life.

Cluster #4—Research on technology-assisted home care for older adults is gaining popularity. Environmentally assisted living enhances older adults’ independence and comfort at home, offering essential support and security. This has a crucial impact on promoting healthy aging (Friesen et al. 2016 ; Wahlroos et al. 2023 ). The smart home is a core application in this field, providing a range of solutions that facilitate independent living for the elderly in a highly integrated and user-friendly manner. This fulfills different dimensions of living and health needs (Majumder et al. 2017 ). Moreover, eHealth offers accurate and personalized health management and healthcare services for older adults (Delmastro et al. 2018 ), ensuring their needs are met at home. Research in this field often employs qualitative methods and structural equation modeling to fully understand older adults’ needs and experiences at home and analyze factors influencing technology adoption.

Evolutionary trends analysis

To gain a deeper understanding of the evolutionary trends in research hotspots within the field of older adults’ technology acceptance, we conducted a statistical analysis of the average appearance times of keywords, using CiteSpace to generate the time-zone evolution mapping (Fig. 8 ) and burst keywords. The time-zone mapping visually displays the evolution of keywords over time, intuitively reflecting the frequency and initial appearance of keywords in research, commonly used to identify trends in research topics (Jing et al. 2024a ; Kumar et al. 2021 ). Table 10 lists the top 15 keywords by burst strength, with the red sections indicating high-frequency citations and their burst strength in specific years. These burst keywords reveal the focus and trends of research themes over different periods (Kleinberg 2002 ). Combining insights from the time-zone mapping and burst keywords provides more objective and accurate research insights (Wang et al. 2023b ).

Reflecting the frequency and time of first appearance of keywords in the study.

An integrated analysis of Fig. 8 and Table 10 shows that early research on older adults’ technology acceptance primarily focused on factors such as perceived usefulness, ease of use, and attitudes towards information technology, including their use of computers and the internet (Pan and Jordan-Marsh 2010 ), as well as differences in technology use between older adults and other age groups (Guner and Acarturk 2020 ). Subsequently, the research focus expanded to improving the quality of life for older adults, exploring how technology can optimize health management and enhance the possibility of independent living, emphasizing the significant role of technology in improving the quality of life for the elderly. With ongoing technological advancements, recent research has shifted towards areas such as “virtual reality,” “telehealth,” and “human-robot interaction,” with a focus on the user experience of older adults (Halim et al. 2022 ). The appearance of keywords such as “physical activity” and “exercise” highlights the value of technology in promoting physical activity and health among older adults. This phase of research tends to make cutting-edge technology genuinely serve the practical needs of older adults, achieving its widespread application in daily life. Additionally, research has focused on expanding and quantifying theoretical models of older adults’ technology acceptance, involving keywords such as “perceived risk”, “validation” and “UTAUT”.

In summary, from 2013 to 2023, the field of older adults’ technology acceptance has evolved from initial explorations of influencing factors, to comprehensive enhancements in quality of life and health management, and further to the application and deepening of theoretical models and cutting-edge technologies. This research not only reflects the diversity and complexity of the field but also demonstrates a comprehensive and in-depth understanding of older adults’ interactions with technology across various life scenarios and needs.

Research quality distribution

To reveal the distribution of research quality in the field of older adults’ technology acceptance, a strategic diagram analysis is employed to calculate and illustrate the internal development and interrelationships among various research themes (Xie et al. 2020 ). The strategic diagram uses Centrality as the X-axis and Density as the Y-axis to divide into four quadrants, where the X-axis represents the strength of the connection between thematic clusters and other themes, with higher values indicating a central position in the research field; the Y-axis indicates the level of development within the thematic clusters, with higher values denoting a more mature and widely recognized field (Li and Zhou 2020 ).

Through cluster analysis and manual verification, this study categorized 61 core keywords (Frequency ≥5) into 11 thematic clusters. Subsequently, based on the keywords covered by each thematic cluster, the research themes and their directions for each cluster were summarized (Table 11 ), and the centrality and density coordinates for each cluster were precisely calculated (Table 12 ). Finally, a strategic diagram of the older adults’ technology acceptance research field was constructed (Fig. 9 ). Based on the distribution of thematic clusters across the quadrants in the strategic diagram, the structure and developmental trends of the field were interpreted.

Classification and visualization of theme clusters based on density and centrality.

As illustrated in Fig. 9 , (1) the theme clusters of #3 Usage Experience and #4 Assisted Living Technology are in the first quadrant, characterized by high centrality and density. Their internal cohesion and close links with other themes indicate their mature development, systematic research content or directions have been formed, and they have a significant influence on other themes. These themes play a central role in the field of older adults’ technology acceptance and have promising prospects. (2) The theme clusters of #6 Smart Devices, #9 Theoretical Models, and #10 Mobile Health Applications are in the second quadrant, with higher density but lower centrality. These themes have strong internal connections but weaker external links, indicating that these three themes have received widespread attention from researchers and have been the subject of related research, but more as self-contained systems and exhibit independence. Therefore, future research should further explore in-depth cooperation and cross-application with other themes. (3) The theme clusters of #7 Human-Robot Interaction, #8 Characteristics of the Elderly, and #11 Research Methods are in the third quadrant, with lower centrality and density. These themes are loosely connected internally and have weak links with others, indicating their developmental immaturity. Compared to other topics, they belong to the lower attention edge and niche themes, and there is a need for further investigation. (4) The theme clusters of #1 Digital Healthcare Technology, #2 Psychological Factors, and #5 Socio-Cultural Factors are located in the fourth quadrant, with high centrality but low density. Although closely associated with other research themes, the internal cohesion within these clusters is relatively weak. This suggests that while these themes are closely linked to other research areas, their own development remains underdeveloped, indicating a core immaturity. Nevertheless, these themes are crucial within the research domain of elderly technology acceptance and possess significant potential for future exploration.

Discussion on distribution power (RQ1)

Over the past decade, academic interest and influence in the area of older adults’ technology acceptance have significantly increased. This trend is evidenced by a quantitative analysis of publication and citation volumes, particularly noticeable in 2019 and 2022, where there was a substantial rise in both metrics. The rise is closely linked to the widespread adoption of emerging technologies such as smart homes, wearable devices, and telemedicine among older adults. While these technologies have enhanced their quality of life, they also pose numerous challenges, sparking extensive research into their acceptance, usage behaviors, and influencing factors among the older adults (Pirzada et al. 2022 ; Garcia Reyes et al. 2023 ). Furthermore, the COVID-19 pandemic led to a surge in technology demand among older adults, especially in areas like medical consultation, online socialization, and health management, further highlighting the importance and challenges of technology. Health risks and social isolation have compelled older adults to rely on technology for daily activities, accelerating its adoption and application within this demographic. This phenomenon has made technology acceptance a critical issue, driving societal and academic focus on the study of technology acceptance among older adults.

The flow of knowledge at the level of high-output disciplines and journals, along with the primary publishing outlets, indicates the highly interdisciplinary nature of research into older adults’ technology acceptance. This reflects the complexity and breadth of issues related to older adults’ technology acceptance, necessitating the integration of multidisciplinary knowledge and approaches. Currently, research is primarily focused on medical health and human-computer interaction, demonstrating academic interest in improving health and quality of life for older adults and addressing the urgent needs related to their interactions with technology. In the field of medical health, research aims to provide advanced and innovative healthcare technologies and services to meet the challenges of an aging population while improving the quality of life for older adults (Abdi et al. 2020 ; Wilson et al. 2021 ). In the field of human-computer interaction, research is focused on developing smarter and more user-friendly interaction models to meet the needs of older adults in the digital age, enabling them to actively participate in social activities and enjoy a higher quality of life (Sayago, 2019 ). These studies are crucial for addressing the challenges faced by aging societies, providing increased support and opportunities for the health, welfare, and social participation of older adults.

Discussion on research power (RQ2)

This study analyzes leading countries and collaboration networks, core institutions and authors, revealing the global research landscape and distribution of research strength in the field of older adults’ technology acceptance, and presents quantitative data on global research trends. From the analysis of country distribution and collaborations, China and the USA hold dominant positions in this field, with developed countries like the UK, Germany, Italy, and the Netherlands also excelling in international cooperation and research influence. The significant investment in technological research and the focus on the technological needs of older adults by many developed countries reflect their rapidly aging societies, policy support, and resource allocation.

China is the only developing country that has become a major contributor in this field, indicating its growing research capabilities and high priority given to aging societies and technological innovation. Additionally, China has close collaborations with countries such as USA, the UK, and Malaysia, driven not only by technological research needs but also by shared challenges and complementarities in aging issues among these nations. For instance, the UK has extensive experience in social welfare and aging research, providing valuable theoretical guidance and practical experience. International collaborations, aimed at addressing the challenges of aging, integrate the strengths of various countries, advancing in-depth and widespread development in the research of technology acceptance among older adults.

At the institutional and author level, City University of Hong Kong leads in publication volume, with research teams led by Chan and Chen demonstrating significant academic activity and contributions. Their research primarily focuses on older adults’ acceptance and usage behaviors of various technologies, including smartphones, smart wearables, and social robots (Chen et al. 2015 ; Li et al. 2019 ; Ma et al. 2016 ). These studies, targeting specific needs and product characteristics of older adults, have developed new models of technology acceptance based on existing frameworks, enhancing the integration of these technologies into their daily lives and laying a foundation for further advancements in the field. Although Tilburg University has a smaller publication output, it holds significant influence in the field of older adults’ technology acceptance. Particularly, the high citation rate of Peek’s studies highlights their excellence in research. Peek extensively explored older adults’ acceptance and usage of home care technologies, revealing the complexity and dynamics of their technology use behaviors. His research spans from identifying systemic influencing factors (Peek et al. 2014 ; Peek et al. 2016 ), emphasizing familial impacts (Luijkx et al. 2015 ), to constructing comprehensive models (Peek et al. 2017 ), and examining the dynamics of long-term usage (Peek et al. 2019 ), fully reflecting the evolving technology landscape and the changing needs of older adults. Additionally, the ongoing contributions of researchers like Ziefle, Rogers, and Wouters in the field of older adults’ technology acceptance demonstrate their research influence and leadership. These researchers have significantly enriched the knowledge base in this area with their diverse perspectives. For instance, Ziefle has uncovered the complex attitudes of older adults towards technology usage, especially the trade-offs between privacy and security, and how different types of activities affect their privacy needs (Maidhof et al. 2023 ; Mujirishvili et al. 2023 ; Schomakers and Ziefle 2023 ; Wilkowska et al. 2022 ), reflecting a deep exploration and ongoing innovation in the field of older adults’ technology acceptance.

Discussion on knowledge base and thematic progress (RQ3)

Through co-citation analysis and systematic review of seminal literature, this study reveals the knowledge foundation and thematic progress in the field of older adults’ technology acceptance. Co-citation networks and cluster analyses illustrate the structural themes of the research, delineating the differentiation and boundaries within this field. Additionally, burst detection analysis offers a valuable perspective for understanding the thematic evolution in the field of technology acceptance among older adults. The development and innovation of theoretical models are foundational to this research. Researchers enhance the explanatory power of constructed models by deepening and expanding existing technology acceptance theories to address theoretical limitations. For instance, Heerink et al. ( 2010 ) modified and expanded the UTAUT model by integrating functional assessment and social interaction variables to create the almere model. This model significantly enhances the ability to explain the intentions of older users in utilizing assistive social agents and improves the explanation of actual usage behaviors. Additionally, Chen and Chan ( 2014 ) extended the TAM to include age-related health and capability features of older adults, creating the STAM, which substantially improves predictions of older adults’ technology usage behaviors. Personal attributes, health and capability features, and facilitating conditions have a direct impact on technology acceptance. These factors more effectively predict older adults’ technology usage behaviors than traditional attitudinal factors.

With the advancement of technology and the application of emerging technologies, new research topics have emerged, increasingly focusing on older adults’ acceptance and use of these technologies. Prior to this, the study by Mitzner et al. ( 2010 ) challenged the stereotype of older adults’ conservative attitudes towards technology, highlighting the central roles of usability and usefulness in the technology acceptance process. This discovery laid an important foundation for subsequent research. Research fields such as “smart home technology,” “social life,” and “customer service” are emerging, indicating a shift in focus towards the practical and social applications of technology in older adults’ lives. Research not only focuses on the technology itself but also on how these technologies integrate into older adults’ daily lives and how they can improve the quality of life through technology. For instance, studies such as those by Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ) have explored factors influencing older adults’ use of smartphones, mHealth, and smart wearable devices.

Furthermore, the diversification of research methodologies and innovation in evaluation techniques, such as the use of mixed methods, structural equation modeling (SEM), and neural network (NN) approaches, have enhanced the rigor and reliability of the findings, enabling more precise identification of the factors and mechanisms influencing technology acceptance. Talukder et al. ( 2020 ) employed an effective multimethodological strategy by integrating SEM and NN to leverage the complementary strengths of both approaches, thus overcoming their individual limitations and more accurately analyzing and predicting older adults’ acceptance of wearable health technologies (WHT). SEM is utilized to assess the determinants’ impact on the adoption of WHT, while neural network models validate SEM outcomes and predict the significance of key determinants. This combined approach not only boosts the models’ reliability and explanatory power but also provides a nuanced understanding of the motivations and barriers behind older adults’ acceptance of WHT, offering deep research insights.

Overall, co-citation analysis of the literature in the field of older adults’ technology acceptance has uncovered deeper theoretical modeling and empirical studies on emerging technologies, while emphasizing the importance of research methodological and evaluation innovations in understanding complex social science issues. These findings are crucial for guiding the design and marketing strategies of future technology products, especially in the rapidly growing market of older adults.

Discussion on research hotspots and evolutionary trends (RQ4)

By analyzing core keywords, we can gain deep insights into the hot topics, evolutionary trends, and quality distribution of research in the field of older adults’ technology acceptance. The frequent occurrence of the keywords “TAM” and “UTAUT” indicates that the applicability and theoretical extension of existing technology acceptance models among older adults remain a focal point in academia. This phenomenon underscores the enduring influence of the studies by Davis ( 1989 ) and Venkatesh et al. ( 2003 ), whose models provide a robust theoretical framework for explaining and predicting older adults’ acceptance and usage of emerging technologies. With the widespread application of artificial intelligence (AI) and big data technologies, these theoretical models have incorporated new variables such as perceived risk, trust, and privacy issues (Amin et al. 2024 ; Chen et al. 2024 ; Jing et al. 2024b ; Seibert et al. 2021 ; Wang et al. 2024b ), advancing the theoretical depth and empirical research in this field.

Keyword co-occurrence cluster analysis has revealed multiple research hotspots in the field, including factors influencing technology adoption, interactive experiences between older adults and assistive technologies, the application of mobile health technology in health management, and technology-assisted home care. These studies primarily focus on enhancing the quality of life and health management of older adults through emerging technologies, particularly in the areas of ambient assisted living, smart health monitoring, and intelligent medical care. In these domains, the role of AI technology is increasingly significant (Qian et al. 2021 ; Ho 2020 ). With the evolution of next-generation information technologies, AI is increasingly integrated into elder care systems, offering intelligent, efficient, and personalized service solutions by analyzing the lifestyles and health conditions of older adults. This integration aims to enhance older adults’ quality of life in aspects such as health monitoring and alerts, rehabilitation assistance, daily health management, and emotional support (Lee et al. 2023 ). A survey indicates that 83% of older adults prefer AI-driven solutions when selecting smart products, demonstrating the increasing acceptance of AI in elder care (Zhao and Li 2024 ). Integrating AI into elder care presents both opportunities and challenges, particularly in terms of user acceptance, trust, and long-term usage effects, which warrant further exploration (Mhlanga 2023 ). These studies will help better understand the profound impact of AI technology on the lifestyles of older adults and provide critical references for optimizing AI-driven elder care services.

The Time-zone evolution mapping and burst keyword analysis further reveal the evolutionary trends of research hotspots. Early studies focused on basic technology acceptance models and user perceptions, later expanding to include quality of life and health management. In recent years, research has increasingly focused on cutting-edge technologies such as virtual reality, telehealth, and human-robot interaction, with a concurrent emphasis on the user experience of older adults. This evolutionary process demonstrates a deepening shift from theoretical models to practical applications, underscoring the significant role of technology in enhancing the quality of life for older adults. Furthermore, the strategic coordinate mapping analysis clearly demonstrates the development and mutual influence of different research themes. High centrality and density in the themes of Usage Experience and Assisted Living Technology indicate their mature research status and significant impact on other themes. The themes of Smart Devices, Theoretical Models, and Mobile Health Applications demonstrate self-contained research trends. The themes of Human-Robot Interaction, Characteristics of the Elderly, and Research Methods are not yet mature, but they hold potential for development. Themes of Digital Healthcare Technology, Psychological Factors, and Socio-Cultural Factors are closely related to other themes, displaying core immaturity but significant potential.

In summary, the research hotspots in the field of older adults’ technology acceptance are diverse and dynamic, demonstrating the academic community’s profound understanding of how older adults interact with technology across various life contexts and needs. Under the influence of AI and big data, research should continue to focus on the application of emerging technologies among older adults, exploring in depth how they adapt to and effectively use these technologies. This not only enhances the quality of life and healthcare experiences for older adults but also drives ongoing innovation and development in this field.

Research agenda

Based on the above research findings, to further understand and promote technology acceptance and usage among older adults, we recommend future studies focus on refining theoretical models, exploring long-term usage, and assessing user experience in the following detailed aspects:

Refinement and validation of specific technology acceptance models for older adults: Future research should focus on developing and validating technology acceptance models based on individual characteristics, particularly considering variations in technology acceptance among older adults across different educational levels and cultural backgrounds. This includes factors such as age, gender, educational background, and cultural differences. Additionally, research should examine how well specific technologies, such as wearable devices and mobile health applications, meet the needs of older adults. Building on existing theoretical models, this research should integrate insights from multiple disciplines such as psychology, sociology, design, and engineering through interdisciplinary collaboration to create more accurate and comprehensive models, which should then be validated in relevant contexts.

Deepening the exploration of the relationship between long-term technology use and quality of life among older adults: The acceptance and use of technology by users is a complex and dynamic process (Seuwou et al. 2016 ). Existing research predominantly focuses on older adults’ initial acceptance or short-term use of new technologies; however, the impact of long-term use on their quality of life and health is more significant. Future research should focus on the evolution of older adults’ experiences and needs during long-term technology usage, and the enduring effects of technology on their social interactions, mental health, and life satisfaction. Through longitudinal studies and qualitative analysis, this research reveals the specific needs and challenges of older adults in long-term technology use, providing a basis for developing technologies and strategies that better meet their requirements. This understanding aids in comprehensively assessing the impact of technology on older adults’ quality of life and guiding the optimization and improvement of technological products.

Evaluating the Importance of User Experience in Research on Older Adults’ Technology Acceptance: Understanding the mechanisms of information technology acceptance and use is central to human-computer interaction research. Although technology acceptance models and user experience models differ in objectives, they share many potential intersections. Technology acceptance research focuses on structured prediction and assessment, while user experience research concentrates on interpreting design impacts and new frameworks. Integrating user experience to assess older adults’ acceptance of technology products and systems is crucial (Codfrey et al. 2022 ; Wang et al. 2019 ), particularly for older users, where specific product designs should emphasize practicality and usability (Fisk et al. 2020 ). Researchers need to explore innovative age-appropriate design methods to enhance older adults’ usage experience. This includes studying older users’ actual usage preferences and behaviors, optimizing user interfaces, and interaction designs. Integrating feedback from older adults to tailor products to their needs can further promote their acceptance and continued use of technology products.

Conclusions

This study conducted a systematic review of the literature on older adults’ technology acceptance over the past decade through bibliometric analysis, focusing on the distribution power, research power, knowledge base and theme progress, research hotspots, evolutionary trends, and quality distribution. Using a combination of quantitative and qualitative methods, this study has reached the following conclusions:

Technology acceptance among older adults has become a hot topic in the international academic community, involving the integration of knowledge across multiple disciplines, including Medical Informatics, Health Care Sciences Services, and Ergonomics. In terms of journals, “PSYCHOLOGY, EDUCATION, HEALTH” represents a leading field, with key publications including Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction . These journals possess significant academic authority and extensive influence in the field.

Research on technology acceptance among older adults is particularly active in developed countries, with China and USA publishing significantly more than other nations. The Netherlands leads in high average citation rates, indicating the depth and impact of its research. Meanwhile, the UK stands out in terms of international collaboration. At the institutional level, City University of Hong Kong and The University of Hong Kong in China are in leading positions. Tilburg University in the Netherlands demonstrates exceptional research quality through its high average citation count. At the author level, Chen from China has the highest number of publications, while Peek from the Netherlands has the highest average citation count.

Co-citation analysis of references indicates that the knowledge base in this field is divided into three main categories: theoretical model deepening, emerging technology applications, and research methods and evaluation. Seminal literature focuses on four areas: specific technology use by older adults, expansion of theoretical models of technology acceptance, information technology adoption behavior, and research perspectives. Research themes have evolved from initial theoretical deepening and analysis of influencing factors to empirical studies on individual factors and emerging technologies.

Keyword analysis indicates that TAM and UTAUT are the most frequently occurring terms, while “assistive technology” and “virtual reality” are focal points with high frequency and centrality. Keyword clustering analysis reveals that research hotspots are concentrated on the influencing factors of technology adoption, human-robot interaction experiences, mobile health management, and technology for aging in place. Time-zone evolution mapping and burst keyword analysis have revealed the research evolution from preliminary exploration of influencing factors, to enhancements in quality of life and health management, and onto advanced technology applications and deepening of theoretical models. Furthermore, analysis of research quality distribution indicates that Usage Experience and Assisted Living Technology have become core topics, while Smart Devices, Theoretical Models, and Mobile Health Applications point towards future research directions.

Through this study, we have systematically reviewed the dynamics, core issues, and evolutionary trends in the field of older adults’ technology acceptance, constructing a comprehensive Knowledge Mapping of the domain and presenting a clear framework of existing research. This not only lays the foundation for subsequent theoretical discussions and innovative applications in the field but also provides an important reference for relevant scholars.

Limitations

To our knowledge, this is the first bibliometric analysis concerning technology acceptance among older adults, and we adhered strictly to bibliometric standards throughout our research. However, this study relies on the Web of Science Core Collection, and while its authority and breadth are widely recognized, this choice may have missed relevant literature published in other significant databases such as PubMed, Scopus, and Google Scholar, potentially overlooking some critical academic contributions. Moreover, given that our analysis was confined to literature in English, it may not reflect studies published in other languages, somewhat limiting the global representativeness of our data sample.

It is noteworthy that with the rapid development of AI technology, its increasingly widespread application in elderly care services is significantly transforming traditional care models. AI is profoundly altering the lifestyles of the elderly, from health monitoring and smart diagnostics to intelligent home systems and personalized care, significantly enhancing their quality of life and health care standards. The potential for AI technology within the elderly population is immense, and research in this area is rapidly expanding. However, due to the restrictive nature of the search terms used in this study, it did not fully cover research in this critical area, particularly in addressing key issues such as trust, privacy, and ethics.

Consequently, future research should not only expand data sources, incorporating multilingual and multidatabase literature, but also particularly focus on exploring older adults’ acceptance of AI technology and its applications, in order to construct a more comprehensive academic landscape of older adults’ technology acceptance, thereby enriching and extending the knowledge system and academic trends in this field.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/6K0GJH .

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This research was supported by the Social Science Foundation of Shaanxi Province in China (Grant No. 2023J014).

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Shang, X., Liu, Z., Gong, C. et al. Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023. Humanit Soc Sci Commun 11 , 1115 (2024). https://doi.org/10.1057/s41599-024-03658-2

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Artificial intelligence in net-zero carbon emissions for sustainable building projects: a systematic literature and science mapping review.

1. Introduction

• Analyze the annual publication trends of published articles and select peer-reviewed journals on AI in NZCEs for sustainable building projects.
• Apply a science mapping approach to analyze influential keywords and document analyses of AI in NZCEs for sustainable building projects.
• Identify and discuss mainstream research topics related to AI in NZCEs for sustainable building projects.
• Develop a framework for depicting research gaps and future research directions on AI in NZCEs for sustainable building projects.

2. Research Methods

2.1. search strategy, 2.2. selection criteria, 2.3. science mapping analysis, 2.4. qualitative discussion, 3.1. annual publication trend, 3.2. selection of relevant peer-reviewed journals, 3.3. co-occurrence analysis of keywords.

• Building eco-friendly, efficient, and energy-efficient structures can significantly reduce the problems associated with excessive carbon emissions. It has been shown that quantifying and analyzing the carbon footprint of public buildings over their life cycle can reduce negative environmental impacts [ 73 ]. Tushar et al. [ 74 ] applied sensitivity analysis to reduce the carbon footprint, thus improving energy efficiency. Developing implicit databases is also a good way to reduce carbon emissions and can be combined with machine and deep learning algorithms to combat climate change and resource scarcity [ 75 ]. It has also been reported that embodied carbon can be used throughout the life cycle of a building to improve the safety and environmental impact of a building project [ 76 , 77 , 78 , 79 ]. Additionally, the heating and cooling aspects of buildings consume more energy; therefore, the development of intelligent control systems is necessary. To reduce emissions, scalability should be the focus [ 69 ].
• The use of AI to minimize carbon emissions in construction projects is the second cluster of research. AI can be used to create smart energy networks and reduce energy costs [ 80 ]. By applying AI techniques, building energy and carbon footprints can be used to predict energy consumption and CO 2 emissions [ 81 , 82 , 83 ]. Deep learning and ML are branches of AI techniques that are widely used as data analytics techniques for reducing NZCEs for sustainable building projects. For example, ANN has been used to quantify environmental costs in residential buildings and optimize commercial building design [ 84 , 85 ]. To achieve this goal, Palladino [ 86 ] studied the use of ANN in specific energy strategies in the Umbria Region. It has been reported that the application of ML can reduce the power consumption of buildings and help optimize building performance in the design and development of smart buildings [ 87 , 88 ].
• A multi-objective optimization technique is proposed to reduce residential construction carbon emissions, accomplishing the dual goals of economic development and environmental conservation, and conforming to the sustainable development principle [ 89 ]. Multi-objective optimization combined with AI technology, can contribute to the development of sustainable buildings in terms of building material selection, retrofitting energy systems, and decision-making in building construction [ 90 ]. For example, the combination of an ANN with a multi-objective genetic algorithm can optimize the design of residential buildings [ 91 , 92 ]. Clustering techniques are integrated with multi-objective optimization to identify urban structures based on their energy performance. This strategy can be replicated in other cities to increase energy efficiency and execute carbon-cutting initiatives [ 70 ]. Multiple goals can help sustainable buildings achieve NZCEs.
• Improving energy consumption efficiency and strengthening building energy management are critical for mitigating the greenhouse effect and global warming trend [ 93 ]. Reduced carbon emissions, green buildings, and sustainable development have emerged as major concerns worldwide [ 2 , 94 ]. On the one hand, renewable energy-driven building systems based on solar and wind resources can reduce environmental effects and costs [ 95 , 96 ]. Building carbon emissions must be minimized to achieve energy sustainability [ 97 ]. However, focusing on building carbon emissions throughout their life cycle, including the design, transportation, construction, and operation stages, and quantifying them as environmental and carbon costs, can contribute to the long-term development of the construction industry [ 98 ]. In summary, reducing energy consumption can contribute to economic benefits and achieve sustainable development [ 77 , 99 ].
• In the face of serious problems posed by climate change, efficient ways to minimize carbon emissions in the construction sector are receiving considerable attention. China is attempting to assess the feasibility of NZCEs, provide a path to reduce emissions, adjust and optimize the industrial structure, and achieve the policy goals of green development and carbon neutrality [ 1 , 100 ]. The prediction of carbon emission intensity in different countries can help policymakers devise environmental policies to address the adverse environmental effects of climate change [ 101 , 102 ]. Enhancing building management systems and promoting smart buildings will also help reduce the energy footprint and continuously optimize building performance [ 88 ]. Carbon capture and storage technologies currently play an essential role in lowering carbon dioxide emissions; however, they face problems such as high costs and regulatory issues, and related technologies still need to be developed [ 103 ].
• Consider a structural design scheme for upgrading a building based on the decision support system (DSS). Carbon capture and storage technologies have been demonstrated in previous studies [ 104 ]. On the other hand, environmental considerations can be evaluated to assess building sustainability. As a result, the entire decision-making process can be optimized [ 105 ]. Simultaneously, DSS, combined with the predictive capabilities of ML to investigate the proper concrete mix proportions, can aid in assessing the impact of a building over its full life cycle, both in terms of environmental and financial expenses [ 72 , 106 ].

3.4. Document Analysis

4. discussion, 4.1. mainstream research topics on ai in nzces for sustainable building projects, 4.1.1. life cycle assessment and carbon footprint, 4.1.2. practical applications of ai techniques in sustainable buildings, 4.1.3. multi-objective optimization, 4.1.4. energy management and energy efficiency, 4.1.5. carbon emissions from buildings, 4.1.6. decision support system (dss) and sustainability, 4.2. research gaps of ai in nzces for sustainable buildings, 4.2.1. existing problems of the life cycle assessment method, 4.2.2. opportunities and challenges faced by ai techniques in sustainable buildings, 4.2.3. scope of application of multi-objective modeling, 4.2.4. improvements in energy management and efficiency, 4.2.5. raise awareness of reducing carbon emissions, 4.2.6. sustainable development of buildings, 4.3. research trends of ai in nzces for sustainable building projects.

• Various factors, such as energy savings, emissions reduction, and the feasibility of financial costs, should be considered when adopting LCA methods.
• Improving the legal framework and international regulatory regime for the application of AI techniques to reduce carbon emissions.
• Balancing carbon emission reduction with other sustainability objectives in response to changes in building parameters.
• Empirical research on energy optimization strategies for different building scenarios.
• Construction industries and practitioners should actively implement carbon-neutral policies.
• Countries can share their experiences and work together to promote the development of sustainable buildings.
• Using DSS to provide data analyses and forecasts should incorporate more environmental parameters to enable decision-makers to make sustainable development decisions.
• Increased attention to decision-making processes and the implementation of program design to reduce carbon emissions.

5. Conclusions

5.1. study implications and contributions, 5.2. limitations and future research directions, author contributions, data availability statement, acknowledgments, conflicts of interest.

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Li, Y.; Antwi-Afari, M.F.; Anwer, S.; Mehmood, I.; Umer, W.; Mohandes, S.R.; Wuni, I.Y.; Abdul-Rahman, M.; Li, H. Artificial Intelligence in Net-Zero Carbon Emissions for Sustainable Building Projects: A Systematic Literature and Science Mapping Review. Buildings 2024 , 14 , 2752. https://doi.org/10.3390/buildings14092752

Li Y, Antwi-Afari MF, Anwer S, Mehmood I, Umer W, Mohandes SR, Wuni IY, Abdul-Rahman M, Li H. Artificial Intelligence in Net-Zero Carbon Emissions for Sustainable Building Projects: A Systematic Literature and Science Mapping Review. Buildings . 2024; 14(9):2752. https://doi.org/10.3390/buildings14092752

Li, Yanxue, Maxwell Fordjour Antwi-Afari, Shahnawaz Anwer, Imran Mehmood, Waleed Umer, Saeed Reza Mohandes, Ibrahim Yahaya Wuni, Mohammed Abdul-Rahman, and Heng Li. 2024. "Artificial Intelligence in Net-Zero Carbon Emissions for Sustainable Building Projects: A Systematic Literature and Science Mapping Review" Buildings 14, no. 9: 2752. https://doi.org/10.3390/buildings14092752

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Esthetic Management of Multiple Gingival Recession – A Case Report and Review of Literature

Phull, Tanvi 1 ; Garg, Anamika 2 ; Jyoti, Divya 3 ; Yamini, R 2 ; Natso, Vizoto 2 ; Sapra, Jatin 2

1 Department of Oral and Maxillofacial Surgery, Gian Sagar Dental College, Rajpura, Patiala, Punjab, India

2 Department of Periodontology, Government Dental College and Hospital, Patiala, Punjab, India

3 Department of Oral Health Sciences (Periodontics), Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India

Address for correspondence: Dr. Divya Jyoti, Department of Oral Health Sciences (Periodontics), Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India. E-mail: [email protected]

This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 4.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The importance of esthetics is increasing for the patients as well as for the dentist. With a high incidence that rises with age and a complicated soft-tissue pathology caused by a wide variety of factors, gingival recessions are a common but troublesome dental problem. They are characterized by the exposure of the root surface of the teeth due to the apical migration of the gingival border beyond the cemento-enamel junction, and they cause both functional and cosmetic disruptions. Many different surgical approaches have been offered over the years to address gingival recession, all with the goal of providing enough root coverage and achieving aesthetically pleasing results. No matter the amount of defects, Zucchelli’s method is beneficial in terms of root coverage and keratinized tissue gain when treating many neighboring recessions. In this case study, Zucchelli’s coronally advanced flap is used to treat numerous neighboring gingival recessions.

I NTRODUCTION

According to the CEJ, Glossary of Periodontology Terms, AAP, 2001, gingival recession is the apical displacement of the gingival edge in reference to the cementoenamel junction (CEJ). Multiple teeth are often affected at once, and it occurs equally frequently in both those with poor and excellent dental hygiene habits. [ 1 ] If this happens at the front of your mouth, your smile will seem less than perfect.

Many root coverage methods exist for the treatment of gingival recession. There are two types of soft tissue transplant surgeries: those performed with a pedicle and those performed without pedicle. Pedicle soft tissue graft procedures include rotational flap procedures (such as the lateral sliding flap, oblique rotated flap, and double papilla flap), advanced flap procedures (such as the coronally advanced flap and the semilunar coronal advanced flap), and regenerative procedures (using a barrier membrane or by introducing enamel matrix proteins). There are two forms of soft-tissue transplant therapies available at no cost to the patient: the epithelialized graft and the subepithelial connective tissue graft. [ 2 ]

Patients with recession defects that still have some apical keratinized tissue are optimal candidates for a coronally advanced flap.

When just the region directly around the tooth with recession is treated, postoperative pain is minimized. [ 3 ] Treatment of teeth with numerous recession flaws using a modified coronally advanced flap method was introduced by Zucchelli and de Sanctis in 2000. To raise the envelope flap, a split-full-split method was adopted. The envelope flap was moved to the front by making a small incision on the skin’s surface. The incision reduced flap thickness that had been taken up by the insertion of lip muscles. The coronally advanced flap of Zucchelli does not have any vertical release incisions. The split-full-split method results in a flap of varying thickness. Submarginal oblique incisions are another characteristic of Zucchelli’s method, which are made in the interdental space between the CEJ of one tooth and the marginal gingiva of the next tooth. [ 4 ]

C ASE R EPORT

After experiencing gum recession in the upper left front area for 12 months, a 32-year-old woman sought help from the Periodontology Department at the Government Dental College and Hospital in Patiala. No significant personal or family history of illness was present. The patient, a homemaker who did not smoke or otherwise use tobacco products, was determined to have caused her disease by brushing her teeth vigorously twice a day in a horizontal motion [ Figure 1 ].

Surgical procedure

Povidone-iodine 5% was used for extraoral washing, and 0.2% chlorhexidine mouthwash was used for rinsing. The patient was administered a local anesthetic (lignocaine HCL with 2% epinephrine 1:200,000). After administering the local anesthetic, the surgeon made a horizontal incision with a 15c [ Figure 2 ]. blade and created an envelope flap. For simple coronal advancement of the flap to cover the exposed root surfaces, the incision included one tooth on each side of the recessed tooth. Oblique submarginal incisions were made between teeth, and intrasulcular incisions were made over recession faults as part of the horizontal incision. New interdental papillae might develop with the help of the oblique submarginal incisions. The flap was dissected using a split-full split technique with a 15c no. blade. The flap was moved from the coronal plane to the apical plane. A split-thickness dissection was performed on the tissue at the top of the recession. The recession’s apical gingiva was brought up to its maximum thickness. The thick section of the flap required for root covering was created using this full-thickness method. Finally, the apex of the flap was raised using a split-thickness technique. The thickness of the flap was cut in half so that it could be easily repositioned in the coronal plane. Machines equipped with curettes (Hu-Friedy, 2R-2 L, 4R-4 L) were used for the root planing. To provide a surgical bed for the coronally advanced flap, the remaining interdental papillae were deepithelized. Once the exposed root surfaces were covered, the flap was advanced coronally. On a surgical table that had been deepithelized, the freshly produced interdental papillae were rotated. Sutures (Ethicon 4-0) were used to bind the flap in an intermittent fashion. After confirmation of the precise advancement of a flap, periodontal dressing (Coe-Pak) was given to cover the surgical site [Figures 3 and 4 ].

Postoperative care

The patient was warned not to apply any pressure on the surgical wound. For the first five days, the patient was told to avoid eating hard foods and avoid brushing the afflicted area. The prescribed dosages of amoxicillin and ibuprofen are 500 and 400 mg, respectively, twice daily for three days. The doctor also suggested gargling with 0.2% chlorhexidine twice a day for 1 min. After two weeks, the surgical dressing and sutures were taken out. Satisfactory healing along with adequate root coverage was obtained. Recall after 1 month revealed very good results with root coverage and color match of the advanced flap. The root coverage observed was almost 100% in both the teeth after 1 month. Stable results were noticed in both the teeth even after 6 months [ Figure 5 ].

D ISCUSSION

A widely used method of root covering is the coronally advanced flap surgery. The idea behind this treatment is to move soft tissue up and across exposed root surfaces. [ 5 ] To shift the flap coronally and overcome muscular tension on the flap, [ 6 ] periosteal incisions were first employed in most surgeries. By cutting off muscle attachments, the coronally modified flap reported by de Sanctis and Zucchelli in 2000 now allows for coronal displacement. This technique not only eliminates the tension but also provides passive displacement of flap till cementoenamel junction without using sutures as there is an absence of muscle pull. Therefore, this technique achieves stable and better root coverage. A systemic review by Hofmänner et al . [ 7 ] reported that full root coverage acquired with a modified coronally advanced flap was maintained for a period of 5 years in patients with Miller Class I and II repeated gingival recessions. Graziani et al . [ 8 ] conducted a systematic review and meta-analysis that found that the modified coronally advanced flap was more effective in covering the roots of several recessions in the gums. Following these two systemic reviews, the present case report showed better root coverage by Zucchelli’s technique.

Declaration of patient consent

The authors certify that they have obtained all appropriate patient consent forms. In the form, the patient(s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

R EFERENCES

Coronally advanced flap ; gingival recession ; root coverage ; Zucchelli’s technique

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Project portfolio risk management: a structured literature review with future directions for research

2020, International Journal of Information Systems and Project Management

This research was sponsored by the University of Valle, Colombia, and Colfuturo-Colciencias, Colombia.

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